The high proliferation of converter-dominated Distributed Renewable Energy Sources (DRESs) at the distribution grid level has gradually replaced the conventional synchronous generators (SGs) of the transmission system, resulting in emerging stability and security challenges. The inherent characteristics of the SGs are currently used for providing ancillary services (ASs), following the instructions of the Transmission System Operator, while the DRESs are obliged to offer specific system support functions, without being remunerated for these functions, but only for the energy they inject. This changing environment has prompted the integration of energy storage systems as a solution for transfusing new characteristics and elaborating their business in the electricity markets, while the smart grid infrastructure and the upcoming microgrid architectures contribute to the transformation of the distribution grid. This review investigates the existing ASs in transmission system with the respective markets (emphasizing the DRESs’ participation in these markets) and proposes new ASs at distribution grid level, with emphasis to inertial response, active power ramp rate control, frequency response, voltage regulation, fault contribution and harmonic mitigation. The market tools and mechanisms for the procurement of these ASs are presented evolving the existing role of the Operators. Finally, potential barriers in the technical, regulatory, and financial framework have been identified and analyzed.

With transition towards 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future Smart Grids. In this work, we show how emerging 5G mobile cellular network, with its evolution of Machine-Type Communications and the concept of Mobile Edge Computing, provides an adequate environment for distributed monitoring and control tasks in Smart Grids. In particular, we present in detail how Smart Grids could benefit from advanced distributed State Estimation methods placed within 5G environment. We present an overview of emerging distributed State Estimation solutions, focusing on those based on distributed optimization and probabilistic graphical models, and investigate their integration as part of the future 5G Smart Grid services.

In this paper, we propose a multiarea state estimator based on successive convex approximation technique. Our scheme is hybrid since it exploits both non-linear and linear measurements coming from legacy meters and Phasor Measurement Units (PMUs), respectively. The resulting nonconvex optimization problem is solved in an iterative manner. In each iteration, the nonconvex terms in the cost function are approximated by a strongly-convex function. The resulting convex problem is distributedly solved by means of the so-called Alternating Direction Method of Multipliers (ADMM). We also prove convergence to a stationary solution of the original nonconvex state estimation problem. Performance is numerically assessed for the IEEE 14bus and 30-bus test cases. Other optimization schemes from the literature are used as a benchmark.

In this paper, we propose a novel state estimation (SE) scheme for the distribution grid. It exclusively leverages on measurements from µPMUs (micro Phasor Measurement Units) making it possible to operate at a faster time scale than conventional SE approaches based on legacy measurements. To circumvent observability issues, we realize that the voltage drop in adjacent buses is limited and, on that basis, we formulate a SE regularized weighted total variation estimation (WTVSE) problem. The problem can be iteratively solved by resorting to the so-called Alternating Direction Method of Multipliers (ADMM). Further, we provide closed-form expressions for the updates of the primal and dual variables. The performance of the proposed scheme is numerically assessed on a 95-bus distribution system for a number of realistic conditions of noise, load and photovoltaic generation profiles. A number of benchmarks are provided, as well.

In this paper, we propose a regularized state estimation (SE) scheme for the distribution grid. The ultimate goal is to track accurately the system state at a faster time scale according to the requirements of the new operational environment. The SE algorithm operates at two different time scales in which the set of available measurements are different. At the main time instants (every 15 minutes) the set of observations comprises SCADA measurements, pseudomeasurements and micro Phasor Measurement Units (µPMUs). In this case, we resort to a Regularized version of the Normal Equations based SE (R-NESE). In the intermediate time instants, only a reduced number of µPMU measurements is available. To circumvent observability issues, we exploit the fact that the voltage drop in adjacent buses is limited and, on that basis, a regularized weighted total variation estimation (WTVSE) problem is formulated. Then, the impact of in-line voltage regulators (IVLRs) is explicitly taken into consideration and that, forces us to decompose and solve the original SE problem for a number of smaller regions (D-WTVSE). The latter can be iteratively solved by resorting to the Alternating Direction Method of Multipliers (ADMM). Complementarily, we also present a µPMU placement method (µPP) in order to improve the conditioning of the R-NESE problem. This problem can be posed as a mixed integer semidefinite programming model (MISDP) and, thus, can be efficiently solved. The performance of the proposed scheme is numerically assessed on (mostly) a 95-bus distribution system for a number of realistic conditions of noise, load and photovoltaic generation profiles. A number of benchmarks are provided, as well.